An Extended Image Features Based Uncalibrated Visual Servoing Method

doi:10.16182/j.issn1004731x.joss.23-1593

Abstract

Abstract:

Aiming at the traditional uncalibrated visual servo relying on the estimation of image Jacobi matrix and the coupling of the motion of each degree of freedom of the camera, on the basis of image-based uncalibrated visual servo, an extended image features based uncalibrated visual servo method is proposed. By analyzing the relationship between image features and camera frames change in the visual servoing process, the visual servoing process in the image space is decomposed into four basic processes: translation, stretching, rotation and scaling; by analyzing the changing of image features in the visual servoing process, extended image features are used to complement the meaning of traditional image features, image center coordinates, relative length of a straight line, distance between two points, and direction angle are selected to correspond to the motion of each degree of freedom of the camera, and the robot's motion is directly controlled by the error of the image features, which realizes a completely decoupled visual servo that does not depend on the image Jacobian matrix. Comparative simulation experiments on CoppeliaSim platform show that compared with the traditional calibrated visual servoing, the method proposed in this study reduces the image position error, the camera position error, and orientation error by 88%, 94%, and 93% of respectively. The effectiveness of the algorithm is verified using physical experiments.

Key words: robot, uncalibrated visual servoing, extended image feature, feature selection, motion decoupling

CLC Number:

TP242.6

Zhang Shuzhen, Cheng Yukun, Liu Yangbo, Zha Fusheng. An Extended Image Features Based Uncalibrated Visual Servoing Method[J]. Journal of System Simulation, 2025, 37(5): 1210-1221.

Figures/Tables 14

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Table 1

Five visual servo task definitions

任务	初始位姿	期望位姿
1	(0, 0, 0, 0, 0, -1/6 $π$ )	(0, 0, 0, 0, 0, 0 )
2	(0, 0, -1.5, 0, 0, -2/9 $π$ )
3	(-0.08, -0.06, -0.2, 0, 0, -1/6 $π$ )
4	(-0.03, -0.1, -0.2, -1/36 $π$ , -1/9 $π$ , -1/6 $π$ )
5	(-0.2, 0.03, -0.2, 1/24 $π$ , 3/20 $π$ , -1/6 $π$ )

Table 1

Table 2

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算法	误差类型	任务1	任务2	任务3	任务4	任务5
IBVS	图像误差/像素	0.000 7	0.004 1	0.003 7	0.007 8	0.009 2
	相机位置误差/mm	0.307 7	1.434 1	1.077 8	3.762 0	4.917 0
	相机姿态误差/(°)	0.002 9	0.013 6	0.007 0	0.029 9	0.042 2
	轨迹长度/mm	5.152 9	152.716 7	238.004 7	267.610 8	322.033 7
E_IBUVS	图像误差/像素	0.000 0	0.000 0	0.000 0	0.000 0	0.001 1
	相机位置误差/mm	0.094 7	0.072 6	0.093 4	0.078 4	0.056 1
	相机姿态误差/(°)	0.000 7	0.000 4	0.000 9	0.000 8	0.002 8
	轨迹长度/mm	1.739 4	151.400 4	240.297 5	240.477 0	306.464 1

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